skip to main content

Explore Research Products in the NSF-PAR

Title: Design and Simulation of a Multi-Robot Architecture for Large-Scale Construction Projects
Large-scale construction projects can benefit from having a team of heterogeneous building robots operating autonomously and cooperatively on unstructured environments. In this work, we propose a flexible system architecture, MARSala, that allows teams of distributed mobile robots to construct motion support structures in large and unstructured environments using purely local interactions. The paper primarily focuses on the deliberative layer of the architecture which provides a means for formulating a construction project as a motion support structure construction problem. We implemented the architecture in simulation and demonstrated the benefits of such a formulation in two different construction scenarios operating in large unstructured environments.  more » « less
Award ID(s):
2054744 1846340
NSF-PAR ID:
10321604
Author(s) / Creator(s):
;
Date Published:
Journal Name:
2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ( , 2020 IEEE International Conference on Robotics and Automation (ICRA))
    null (Ed.)
    The ability to autonomously modify their environment dramatically increases the capability of robots to operate in unstructured environments. We develop a specialized construction algorithm and robotic system that can autonomously build motion support structures with previously unseen objects. The approach is based on our prior work on adaptive ramp building algorithms, but it eliminates the assumption of having specialized building materials that simplify manipulation and planning for stability. Utilizing irregularly shaped stones makes the problem significantly more challenging since the outcome of individual placements is sensitive to details of contact geometry and friction, which are difficult to observe. To reuse the same high-level algorithm, we develop a new physics-based planner that explicitly considers the uncertainty produced by incomplete in-situ sensing and imprecision during pickup and placement. We demonstrate the approach on a robotic system that uses a newly developed gripper to reliably pick up stones with minimal additional sensors or complex grasp planning. The resulting system can build structures with more than 70 stones, which in turn provide traversable paths to previously inaccessible locations. 
    more » « less
  2. ; ; ( , Frontiers in Robotics and AI)
    The field of Human-Robot Collaboration (HRC) has seen a considerable amount of progress in recent years. Thanks in part to advances in control and perception algorithms, robots have started to work in increasingly unstructured environments, where they operate side by side with humans to achieve shared tasks. However, little progress has been made toward the development of systems that are truly effective in supporting the human, proactive in their collaboration, and that can autonomously take care of part of the task. In this work, we present a collaborative system capable of assisting a human worker despite limited manipulation capabilities, incomplete model of the task, and partial observability of the environment. Our framework leverages information from a high-level, hierarchical model that is shared between the human and robot and that enables transparent synchronization between the peers and mutual understanding of each other’s plan. More precisely, we firstly derive a partially observable Markov model from the high-level task representation; we then use an online Monte-Carlo solver to compute a short-horizon robot-executable plan. The resulting policy is capable of interactive replanning on-the-fly, dynamic error recovery, and identification of hidden user preferences. We demonstrate that the system is capable of robustly providing support to the human in a realistic furniture construction task. 
    more » « less
  3. ; ; ( , SAE Mobilus)
    Traditional ground vehicle architectures comprise of a chassis connected via passive, semi-active, or active suspension systems to multiple ground wheels. Current design-optimizations of vehicle architectures for on-road applications have diminished their mobility and maneuverability in off-road settings. Autonomous Ground Vehicles (AGV) traversing off-road environments face numerous challenges concerning terrain roughness, soil hardness, uneven obstacle-filled terrain, and varying traction conditions. Numerous Active Articulated-Wheeled (AAW) vehicle architectures have emerged to permit AGVs to adapt to variable terrain conditions in various off-road application arenas (off-road, construction, mining, and space robotics). However, a comprehensive framework of AAW platforms for exploring various facets of system architecture/design, analysis (kinematics/dynamics), and control (motions/forces) remains challenging. While current literature on the AAW system incorporates modeling and control from the legged and wheeled-legged robots community, it lacks a systematic process of architecture selection and motion control that should be developed around critical quantifiable performance parameters. This paper will: (i) analyze a broad body of literature; and (ii) identify modeling and control techniques that can enable the efficient development of AAW platforms. We then analyze key performance measures with respect to traversability, maneuverability, and terrainability, along with an experimental simulation of an AAW vehicle traversing over uneven terrain and how active articulation could achieve some of the critical performance measures. Against the performance parameters, gaps within the existing literature and opportunities for further research are identified to potentially enhance AAW platforms’ performance. 
    more » « less
  4. ; ; ( , Field and Service Robotics. Springer Proceedings in Advanced Robotics)
    Ishigami G., Yoshida K. (Ed.)
    This paper develops an autonomous tethered aerial visual assistant for robot operations in unstructured or confined environments. Robotic tele-operation in remote environments is difficult due to the lack of sufficient situational awareness, mostly caused by stationary and limited field-of-view and lack of depth perception from the robot’s onboard camera. The emerging state of the practice is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the onboard sensors by providing an external viewpoint. However, problems exist when using a tele-operated visual assistant: extra manpower, manually chosen suboptimal viewpoint, and extra teamwork demand between primary and secondary operators. In this work, we use an autonomous tethered aerial visual assistant to replace the secondary robot and operator, reducing the human-robot ratio from 2:2 to 1:2. This visual assistant is able to autonomously navigate through unstructured or confined spaces in a risk-aware manner, while continuously maintaining good viewpoint quality to increase the primary operator’s situational awareness. With the proposed co-robots team, tele-operation missions in nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments could benefit from reduced manpower and teamwork demand, along with improved visual assistance quality based on trustworthy risk-aware motion in cluttered environments. 
    more » « less
  5. ; ; ; ; ; ( , Semantic Relation Detection Between Construction Entities to Support Safe Human-Robot Collaboration in Construction)
    Construction robots have drawn increased attention as a potential means of improving construction safety and productivity. However, it is still challenging to ensure safe human-robot collaboration on dynamic and unstructured construction workspaces. On construction sites, multiple entities dynamically collaborate with each other and the situational context between them evolves continually. Construction robots must therefore be equipped to visually understand the scene’s contexts (i.e., semantic relations to surrounding entities), thereby safely collaborating with humans, as a human vision system does. Toward this end, this study builds a unique deep neural network architecture and develops a construction-specialized model by experimenting multiple fine-tuning scenarios. Also, this study evaluates its performance on real construction operations data in order to examine its potential toward real-world applications. The results showed the promising performance of the tuned model: the recall@5 on training and validation dataset reached 92% and 67%, respectively. The proposed method, which supports construction co-robots with the holistic scene understanding, is expected to contribute to promoting safer human-robot collaboration in construction. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email